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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems28%-33%- Operationalizing systems
  • 1. Manage logging and alerting
  • 2. Monitor data pipelines and workloads
  • 3. Implement fault tolerance and recovery
- Building data processing systems
  • 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 2. Implement batch processing systems
  • 3. Implement streaming data systems
Designing data processing systems22%-27%- Designing data pipelines
  • 1. Design orchestration workflows
  • 2. Optimize pipeline performance and cost
  • 3. Build ETL and ELT pipelines
- Designing for data ingestion
  • 1. Design scalable and reliable ingestion architecture
  • 2. Design batch and streaming ingestion solutions
  • 3. Select appropriate storage systems
Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Optimize storage and compute usage
  • 2. Tune query and pipeline performance
  • 3. Control operational costs
- Reliability and scalability
  • 1. Design highly available systems
  • 2. Scale data workloads efficiently
  • 3. Implement disaster recovery strategies
Ensuring solution quality20%-25%- Data quality management
  • 1. Validate and clean data
  • 2. Maintain metadata and lineage
  • 3. Implement data governance
- Security and compliance
  • 1. Manage IAM and access control
  • 2. Implement encryption and compliance controls
  • 3. Protect sensitive data

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Google Data Engineer Sample Questions (Q73-Q78):

NEW QUESTION # 73
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

Answer: E


NEW QUESTION # 74
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

Answer: F


NEW QUESTION # 75
Which of these sources can you not load data into BigQuery from?

Answer: C

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data


NEW QUESTION # 76
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

Answer: D

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers


NEW QUESTION # 77
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

Answer: D

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


NEW QUESTION # 78
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